机器学习高端课程 机器读心术之文本挖掘与自然语言处理，资源教程下载

课程名称

机器学习高端课程 机器读心术之文本挖掘与自然语言处理，资源教程下载

课程目录

│  │  └Tagging with Finite-State Transducers.pdf

│  ├<3>

│  │  ├icslp2002-srilm.pdf

│  │  ├NLP03.pdf

│  │  └NLP03

│  ├<4>

│  │  ├crf-tutorial.pdf

│  │  ├nips01-discriminativegenerative.pdf

│  │  ├NLP04.pdf

│  │  ├NLP04

│  │  ├概率论与数理统计 第四版.pdf

│  │  └概率图模型原理与技术.pdf

│  ├<5>

│  │  ├EM算法详细例子及推导.pdf

│  │  ├hhmm.pdf

│  │  ├htk.rar

│  │  ├HTK.zip

│  │  ├NLP05.pdf

│  │  ├NLP05

│  │  └The-EM-algorithm.pdf

│  ├<6>

│  │  ├Conditional Random Fields An Introduction.pdf

│  │  ├crf.pdf

│  │  ├maxent.pdf

│  │  ├maxent_adwait97.pdf

│  │  ├Maximum Entropy Markov Models for Information Extraction and Segmentation.pdf

│  │  ├NLP06.pdf

│  │  ├NLP06

│  │  ├条件随机场综述.pdf

│  │  └统计学习方法.pdf

│  ├<7>

│  │  ├ActivePerl-5.22.1.2201-MSWin32-x86-64int-299574.msi

│  │  ├conlleval.pl

│  │  ├CRF++-0.58.zip

│  │  ├CRF++工具包使用介绍.ppt

│  │  ├CRF++使用指南.docx

│  │  ├NLP07.pdf

│  │  ├NLP07

│  │  └基于条件随机场的中文人名性别识别研究.doc

│  ├<8>

│  │  ├A Character-Based Joint Model for Chinese Word Segmentation.pdf

│  │  ├A Fast Decoder for Joint Word Segmentation and POS-Tagging Using a Single Discriminative Model.pdf

│  │  ├Chinese Segmentation with aWord-Based Perceptron Algorithm.pdf

│  │  ├Discriminative Training Methods for Hidden Markov Models Theory and Experiments with Perceptron Algorithms.pdf

│  │  ├Integrating Generative and Discriminative Character-Based Models for Chinese Word Segmentation.pdf

│  │  ├NLP08.pdf

│  │  ├NLP08

│  │  ├Syntactic Processing Using the GeneralizedPerceptron and Beam Search.pdf

│  │  ├Which is More Suitable for Chinese Word Segmentation the Generative Model or the Discriminative One.pdf

│  │  ├对自动分词的反思.pdf

│  │  ├基于N-最短路径方法的中文词语粗分模型.pdf

│  │  ├由字构词——中文分词新方法.pdf

│  │  ├中文分词十年回顾.pdf

│  │  └中文文本自动分词和标注_刘开瑛.pdf

│  ├<9>

│  │  ├NLP09.pdf

│  │  ├NLP09

│  │  └汉语问答系统关键技术研究_吴友政.pdf

│  ├<分词算法>

│  │  ├语料库.mp4

│  │  ├语料库建设.pdf

│  │  └

│  ├<10>

│  │  ├LDA及Gibbs-Sampling-yangliuy.pdf

│  │  ├LDA数学八卦.pdf

│  │  ├NLP10.pdf

│  │  ├NLP10

│  │  ├Parameter estimation for text analysis.pdf

│  │  ├PLSA及EM算法-yangliuy.pdf

│  │  ├Unsupervised Learning by Probabilistic Latent Semantic Analysis.pdf

│  │  └一种自适应词性标注方法.pdf

│  ├<11>

│  │  ├A Sentimental Education Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts.pdf

│  │  ├Joint Sentiment Topic Model for Sentiment Analysis.pdf

│  │  ├NLP11.pdf

│  │  ├NLP11

│  │  ├pcfgs.pdf

│  │  ├Thumbs up Sentiment Classification using Machine Learning Techniques.pdf

│  │  ├Using_appraisal_groups_for_sentiment_analysis.pdf

│  │  └第11课作业素材.zip

│  ├<12>

│  │  ├A Hybrid Approach to Chinese Base Noun Phrase Chunking.pdf

│  │  ├Catching the Drift Probabilistic Content Models.pdf

│  │  ├Centroid-based summarization of multiple documents.pdf

│  │  ├Coreference Resolution Current Trends and Future Directions.pdf

│  │  ├Fast Methods for Kernel-based Text Analysis.pdf

│  │  ├First-Order Probabilistic Models for Coreference Resolution.pdf

│  │  ├LATENT DIRICHLET LEARNING FOR DOCUMENT SUMMARIZATION.pdf

│  │  ├Layer-Based Dependency Parsing.pdf

│  │  ├NLP12.pdf

│  │  ├NLP12

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